Live interactive training for product managers, senior pms & technical pms
Build and control real AI agents in plain text.
We built the first interactive training designed to let PMs experience AI agents firsthand. Our students see live AI agents in action in the first 15 minutes, then design and build their own agent systems using Raw Agent Control (RAC). You define agents, workflows, and tools in plain text without changing code or using an agentic framework.
Run live agents.
Become Agentsable.
100% browser based lab · Zero local installations · Zero coding required
triage_agent.rac
role: support_triage context: billing + churn tickets decision_logic: - if refund_request -> route: finance - if sentiment < 0.3 -> escalate output_contract: ticket_id: string priority: P0 | P1 | P2 guardrails: max_tool_iterations: 3 token_budget: 8000 on_limit: fallback_to_human memory: prune: after_each_step keep: [ticket_id, customer_tier] gate: llm_as_a_judge: strict on_fail: repeat (max 1)
execution trace · live
- 00:00agent.startrole=support_triage
- 00:01tool.callcrm.lookup(ticket=4417)
- 00:02memory.prunedropped 2 of 5 frames
- 00:03tool.callcrm.lookup(ticket=4417)
- 00:04guardrail.hititeration cap 3 reached
- 00:04fallbackescalate → human_in_the_loop
- 00:05judge.gateoutput_contract: PASS
Editing a plain text RAC file steers live multi-agent behavior in real time (zero coding required).
How it works — the 3 pillars
From firsthand agent action to building any AI system you can imagine.
Click each pillar to explore ↓
- Pre wired browser lab — live multi agent systems running inside the first 15 minutes of Week 1.
- Zero corporate IT blockers — no sudo, no Docker runtime, no local Python install.
- Real time behavioral steering — edit the plain text rules, re run, and watch the behavior change.
Run live agents.
In AI agents, the fine details matter more than the code, and PMs own the details.
Programmers focus on code syntax, while PMs know the user context, edge cases, and business boundaries. Because an agent's behavior comes down to its responsibilities, instructions, and guardrails, you can't learn this from slide decks. In our interactive training, you run live agents in the first 15 minutes, watching how they think, fail, and respond to your plain text adjustments in real time.
Weekly format — built for busy pms
Designed for busy Product Managers. Even if you miss the recorded videos, you never fall behind.
Every week follows a four step interactive cycle built to take you from concept to hands on plain text execution in a single sitting:
On Demand Lectures
Async, your pace
Weekly Live Q&A
Async, your pace
Live RAC Build Session
Catch up safety net
Hands on Browser Lab
You take the controls
miss steps 01 — attend 02 + 03 — back to 100% in a single sitting
On Demand Lectures
Recorded videos unlocked weekly.
Short, focused video breakdowns released each week so you can explore the core concepts at your own pace and playback speed.
Weekly Live Q&A
Fast concept recap & live Q&A on Zoom.
Every live session opens with a clear review of the recorded material, then answers your specific architectural questions.
Live RAC Build Session
Watch a live agent system built in plain text.
We build a complete, working example live on screen using the RAC system. Even if you missed the recorded videos, the Q&A and Live RAC Build give you 100% of what you need.
Hands on Browser Lab
Take control by editing plain text.
Immediately after the live build you get a ready to run version of that exact system in your browser lab, and implement it yourself purely by editing the plain text files and testing execution.
The Syllabus — 4 weeks of live control
Four weeks from fragile prompts to production ready agentic workflows.
Week 01 — Agent Brains
From Prompt to Agent: Defining a Structured Plain Text Agent.
Move from fragile chat prompts to repeatable plain text definitions, mapping Role, Context, Decision Logic, and Output Contracts.
Live Lab Challenge
Take over the agent from the live build that is misclassifying edge cases. Fix its reasoning behavior by refining its plain text RAC rules.
Week 02 — Tools & Memory
The RAC Agent Tools, MCP & Memory Definition.
Define external tools, Model Context Protocol (MCP) connections, memory rules, and multi agent handoffs in readable plain text.
Live Lab Challenge
Take the multi agent workflow from the live build and wire its tools, memory rules, and agent to agent handoffs in plain text so the team completes a multi step task end to end.
Week 03 — Failure Patterns
When Agents Fail: Fail Patterns of Agentic Workflows.
Diagnose live execution traces and fix the 4 real world failures: infinite tool loops, context decay, broken data formats, and cascading multi agent errors.
Live Lab Challenge
Inspect a broken multi agent workflow trace from the live build. Diagnose the exact failure pattern and stop the loop by adjusting the plain text definitions.
Week 04 — Production Control
Guardrails, LLM as a Judge & Human in the Loop Systems.
Lock in hard operational boundaries, automated pass or repeat evaluator gates, human in the loop escalation triggers, and a verified specification contract ready for your engineering team.
Live Lab Challenge
Add plain text guardrails, an LLM as a Judge pass or repeat gate, and a Human in the Loop escalation rule to the live build example. Practice the controls needed for production readiness.
Built for modern engineering handoffs: Your plain text RAC definitions act as a verified architectural specification contract ready for your engineering team, Cursor, Claude Code, and GitHub SpecKit with zero framework lock in.
Why agents fail — real world reliability
Most AI courses only show you what happens when things work. We train you for when they break.
Anyone can build an agent that looks like magic on Friday in front of a VP, only to watch it hit messy customer data on Monday morning, loop in circles, and burn through thousands of dollars in API tokens. In our interactive training, we intentionally trigger real world breakdowns inside your browser lab so you learn how to diagnose and fix the 4 failure patterns that ruin production AI systems, purely through plain text RAC definitions:
01 Failure Pattern: Infinite Loops
The agent goes in circles.
When an agent receives ambiguous data or an unexpected tool response, it repeats the exact same tool call endlessly until your token budget burns out.
The Plain Text RAC Fix: Define strict iteration caps, retry limits, and fallback instructions outside the prompt.
02 Failure Pattern: Context Decay
The agent forgets its own rules mid task.
As multi step workflows grow longer, bloated memory causes attention degradation, making the agent drop critical instructions and ignore business boundaries.
The Plain Text RAC Fix: Structure memory rules that control exactly what context is kept or pruned at every step.
03 Failure Pattern: Silent Hallucinations
The agent invents fake data to move forward.
Instead of flagging missing information, a probabilistic agent will silently hallucinate parameters or break the output format expected by the next step.
The Plain Text RAC Fix: Lock in strict output contracts and automated LLM as a Judge pass or repeat gates.
04 Failure Pattern: Cascading Errors
One bad step poisons the entire workflow.
In a multi agent workflow, a small mistake by the first agent gets treated as absolute fact by downstream agents, snowballing into a completely broken outcome.
The Plain Text RAC Fix: Wire verification handoffs and Human in the Loop escalation gates before high stakes actions.
iteration_limit: 3
memory: prune_context
gate: llm_as_a_judge
Week 03 — you inspect this trace and stop it with plain text, not code.
The alternatives — and why they fall short
Three ways PMs try to learn agents. All three leave you dependent.
Zapier, Make, n8n
Visual no code builders
Hide agent reasoning inside a monolithic visual black box, fail to track cleanly in Git, and get dismissed by senior engineers as toy flowcharts.
Python, LangChain, Docker
Heavy coding frameworks
High syntax friction for PMs, and blocked outright by CISO laptop policies that prohibit local installs and container runtimes.
$1,000 to $4,000 programs
Theoretical cohorts
Overpriced passive slide decks and generic frameworks that never put a student at the controls of a live agent.
The unclaimed position
Full architectural transparency and live execution with zero syntax overhead. Traditional cohorts charge $1,000 to $4,000 for passive slide decks. Automating the browser lab strips out roughly $2,000 of passive academic overhead — which is how the Super Early Bird seat lands at $100 instead of $300.
The transformation — before & after
Master the AI wave. Become Agentsable.
Same sharp product instincts, now backed by the power to build, debug, and ship real AI agent systems in plain text.
Enrollment details — start building
Join the four week interactive training, live and hands on.
Run your first live agent in minute 15, master Raw Agent Control (RAC) in plain text, and walk away ready to build any AI system you can imagine. We stripped out thousands of dollars of passive slide lectures to deliver 100% live execution inside an automated browser lab, priced to fit directly inside your auto approved corporate learning stipend.
What a seat includes
4 weeks of live interactive training
Weekly live Zoom concept recap, Q&A, and a Live RAC Build Session where a complete working multi agent system is built from scratch in plain text.
Pre wired zero setup browser lab
Runs 100% in the browser. No local Python, no Docker, no terminal, no corporate IT security ticket.
On demand recorded concept library
Short focused video breakdowns unlocked weekly, built for self paced viewing at 1.75x.
The plain text RAC blueprint suite
Reusable templates for Agent Brains, MCP Tool Routers, Memory Pruning Policies, Hard Guardrails, and LLM as a Judge evaluation rubrics.
Instant B2B receipt + 1 click reimbursement kit
A tax receipt and a copy paste corporate L&D justification template issued with every enrollment.
Super Early Bird · one time payment